# coolrl-lost-cities

Focused Lost Cities extraction from the legacy `coolrl` repository.

The current implementation starts with the classic two-player card game:

- classic 5-expedition rules by default
- Python/Cython game engine
- env wrapper
- random, discard-only, and safe-heuristic bots
- core rule, scoring, mask, env, canonical-state, bot, and GUI smoke tests

Training code, Deep CFR, learned-policy evaluation, GUI, and web client are
intentionally outside the first port.

## Development

```bash
uv run pytest tests/games/classic
uv run lost-cities-classic
```

For future GUI work, install the optional GUI dependencies:

```bash
uv sync --extra gui
```

Run the classic pygame GUI:

```bash
uv run lost-cities-classic-gui --mode pvc --bot safe-heuristic
```

The GUI uses the in-process Cython game engine.

## JAX Rules Engine

`lost_cities_jax` is a standalone pure rules simulator for one two-player
Lost Cities round. It does not contain neural networks, PPO, CFR, MCTS, match
wrappers, bots, or rule variants.

Public API:

```python
from lost_cities_jax import (
    OBS_DIM,
    N_ACTIONS,
    State,
    batched_legal_mask,
    batched_obs,
    batched_reset,
    batched_step,
    board_score,
    legal_action_mask,
    observation,
    reset,
    reset_from_order,
    score,
    step,
)
```

Core functions are pure JAX functions:

- `reset(rng) -> State`
- `reset_from_order(deck_order) -> State`
- `legal_action_mask(state) -> bool[96]`
- `step(state, action) -> (State, float32[2], bool)`
- `score(state) -> float32[2]`
- `board_score(state) -> float32[2]`
- `observation(state, player) -> float32[454]`

The batched exports are `jax.jit(jax.vmap(...))` wrappers. Illegal actions and
done-state actions are defined as no-op transitions with zero reward; training
code should still sample only from `legal_action_mask`.

### Rule Summary

One round uses 60 cards: five colors, each with three handshakes and ranks
2 through 10. Each player starts with eight cards, then each ply must place one
hand card to the matching expedition or discard pile and draw one card from the
deck or a discard pile. A player may not draw the card they just discarded.

Expedition numbers must be strictly increasing. Handshakes may be played only
before any number in that color. The round ends immediately when the final deck
card is drawn, or at `MAX_STEPS == 400`; forced termination is scored exactly
like natural termination.

Scoring per player/color:

```text
empty column: 0
non-empty: (sum(number ranks) - 20) * (1 + handshake_count)
bonus: +20 if total column length >= 8, not multiplied
```

### Encodings

Cards:

| Field | Encoding |
| --- | --- |
| `card_id` | `color * 12 + slot` |
| `color` | `0..4` |
| `slot 0..2` | handshake |
| `slot 3..11` | ranks `2..10`, with `rank = slot - 1` |

Actions (`N_ACTIONS == 96`):

```text
action_id = hand_slot * 12 + place_type * 6 + draw_source
hand_slot   = 0..7, current player's hand sorted by card_id
place_type  = 0 play, 1 discard
draw_source = 0 deck, 1..5 discard pile color 0..4
```

Observation (`OBS_DIM == 454`):

- 60 cards x 7 one-hot channels:
  my hand, my board, opponent board, discard top, discard non-top,
  opponent public hand, unknown.
- 34 scalar features:
  remaining deck `/44`, opponent unknown hand count `/8`, step count `/400`,
  current-player then opponent `col_top /10`, `col_hs /3`, `col_len /12`,
  and current board score difference `(player - opponent) /780`.

### Verification

```bash
uv run pytest -q tests/lost_cities_jax
uv run pytest -q
uv run ruff check .
```

Large differential profiles:

```bash
# CI profile: 100,000 random legal-policy games
CI=1 uv run pytest -q tests/lost_cities_jax/test_differential.py

# Full profile: 1,000,000 random legal-policy games
uv run pytest -q tests/lost_cities_jax/test_differential.py --full
```

Observed differential results on 2026-07-04 with CPU JAX backend:

```text
CI=1 ... test_differential.py
1 passed in 247.61s (0:04:07)

... test_differential.py --full
1 passed in 2514.14s (0:41:54)
elapsed=41:54.48
```

Throughput benchmark:

```bash
flock -n .compute.lock uv run python benchmarks/throughput.py

# Optional CUDA check without making CUDA a project dependency:
flock -n .compute.lock uv run --with 'jax[cuda12]' python benchmarks/throughput.py
```

Measured on 2026-07-04 with CPU JAX backend:

```text
backend=cpu
batch_size=8192
steps=256
elapsed_sec=4.655526
steps_per_sec=450465.14
```

Measured on 2026-07-04 with CUDA JAX backend on RTX 3090, using the optional
`uv run --with 'jax[cuda12]' ...` command:

```text
backend=gpu
batch_size=8192
steps=256
elapsed_sec=0.530039
steps_per_sec=3956598.38
```

### DECISIONS.md

- Explicit `deck_order` dealing uses the first eight cards for player 0 and
  the next eight for player 1. The remaining cards are drawn from index 16.
  This is equivalent under a uniform shuffle and is fixed by tests.
- After a legal terminal transition, `to_move` is advanced to the next player,
  but `done=True` makes all later steps complete no-ops.
- Terminal reward is emitted only on the transition that reaches `done=True`.
  Done-state no-op steps return zero reward.
- Observation scalar normalization is implementation-defined as documented
  above and locked by the exported `OBS_DIM`.

## JAX PPO Static-Opponent Ladder

The first training stack above `lost_cities_jax` is exposed as:

```bash
uv run lost-cities-jax-ppo --help
```

CPU smoke:

```bash
uv run lost-cities-jax-ppo rollout-smoke --config configs/jax_ppo/smoke.yaml
uv run lost-cities-jax-ppo train --config configs/jax_ppo/smoke.yaml
```

GPU training uses optional CUDA JAX, keeping CUDA wheels out of the default
project dependency set:

```bash
tmux new-session -s coolrl-jax-ppo-discard \
  -c /home/coolguy/dev/coolrl-lost-cities \
  "flock -n .compute.lock uv run --with 'jax[cuda12]' lost-cities-jax-ppo train \
    --config configs/jax_ppo/discard-only.yaml"
```

Random-policy baseline vs `discard_only`, measured on 2026-07-04 with
8192 games x 400 plies on GPU:

```text
return_mean=-55.582763671875
game_length_mean=69.6085205078125
max_steps_rate=0.0
play_action_rate=0.28854578733444214
opened_colors_mean=4.942626953125
positive_expeditions_mean=0.41796875
```

Large PPO artifacts are written under
`/mnt/2tbhdd/coolrl-lost-cities-artifacts/jax-ppo-static-opponents/`.
Generated checkpoints and evaluation JSON are not committed to git.

## Basic Usage

```python
from coolrl_lost_cities.games.classic import GameState, build_bot, classic_config

state = GameState.new_game(classic_config(seed=1))
bot = build_bot("random", seed=1)

while not state.terminal:
    state.apply_action(bot.act(state))

print(state.total_score(0), state.total_score(1))
```

See [classic port notes](docs/archive/classic-port-notes.md) for the current direction.